Webinar

Abstract der Präsentationen:

[nur in Englisch verfügbar]

Ecological Adaptive Cruise Control for City Buses based on Hybrid Model Predictive Control

This talk introduces an ecological adaptive cruise control (EACC) concept with the primary goal to minimize the fuel consumption in a city bus with an internal combustion engine (ICE). A hybrid model predictive control (HMPC) approach is demonstrated in this talk that is used to control both continuous and discrete-time variables in the system. Moreover, a multi-objective optimization problem for EACC is formulated in time-domain as a mixed-integer quadratically constrained quadratic programming (MIQCQP) problem. The proposed HMPC-EACC performs robust vehicle-following while tracking a leading vehicle and plans fuel-efficient acceleration and deceleration maneuvers for the host vehicle. Additionally, it uses the signal phase and timing (SPaT) information to compute a green wave reference speed for the host vehicle to cross the signalized intersections at a green phase. Moreover, the proposed controller performs pulse and glide (PnG) to optimally control the engine ON and OFF states and save additional fuel. Finally, the influence of different prediction horizons on the fuel savings and computation times are discussed.

Characterising Initial Data for Systems of Partial Differential and Difference Equations

Obtaining explicit solutions of a system of partial differential/difference equations usually requires initial and/or boundary conditions. One of the main challenges in specifying initial and/or boundary conditions for systems of partial differential/difference equations is the lack of natural ordering of the domain. In this talk, we discuss acharacterisation of initial data required to solve a linear system of partial differential/difference equations with real constant coefficients.  We also discuss minimality aspects of the initial data and how the initial data can be used to explicitly solve a given system of partial differential/difference equations.